1. Field of the Invention
The present invention relates to a sleep state estimation device which estimates a sleep state based on vital signs information, and to a program product for executing a sleep state estimation function.
2. Description of the Related Art
Recently heightened health consciousness among people has created a desire to manage their health by day-to-day sleep control in their household. Sleep is divided by type into REM sleep which is a light sleep and non-REM (NREM) sleep which is a deep sleep. A finer sleep classification is Sleep Stage. Sleep Stage is an international standard consisting of “REM sleep”, “Sleep Stages 1, 2, 3 and 4” and “wakefulness”, which are judged from electroencephalogram (EEG), ocular movement, and electric potentials generated by movement of jaw muscles. “Sleep Stages 1, 2, 3 and 4” correspond to NREM sleep during which a person sleeps deep. Polysomography is a known method to judge the sleep state. According to this method, the aforementioned EEG, ocular movement, and electric potentials generated by movement of jaw muscles are detected to judge from the waveforms detected which sleep stage a subject is in.
Other known sleep stage estimation methods than polysomography involve applying the neural network theory, the chaos theory, or the like to measurements of respiration rate, heart rate, and body movement. Those methods are described in JP09-294731 A and on pages 581–589 in Vol. 38, No. 7 of collected papers published by The Society of Instrument and Control Engineers in 2002.
The above-described sleep stage estimation according to prior art places particular emphasis on variations and intervals of heart rate out of respiration rate, heart rate, and body movement information measured. In general, electrocardiogram (ECG) is used to measure heart rate with precision. Measurement by ECG, however, has a drawback in that plural electrodes have to be attached directly to the skin of a subject, restraining the subject with their codes which are connected to ECG equipment. On the other hand, a non-restrictive sensor can only catch minute heart rate signals, which are also full of noises from other elements than heartbeat. Non-restrictive measurement therefore needs FFT and filter computation processing for frequency analysis as well as signal amplification processing, which complicate the measurement process.